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Strålfors, Peter
Alternative names
Publications (10 of 74) Show all publications
Nyman, E., Rozendaal, Y. J., Helmlinger, G., Hamrén, B., Kjellsson, M. C., Strålfors, P., . . . Cedersund, G. (2016). Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes. Interface Focus, 6(2), 1-14
Open this publication in new window or tab >>Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes
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2016 (English)In: Interface Focus, ISSN 2042-8898, E-ISSN 2042-8901, Vol. 6, no 2, p. 1-14Article, review/survey (Refereed) Published
Abstract [en]

We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today's multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them 'personalized' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decision-support systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

Place, publisher, year, edition, pages
London, UK: The Royal Society Publishing, 2016
Keywords
mathematical modelling, systems pharmacology, disease progression, decision-support type 2 diabetes, anti-diabetic treatment
National Category
Biophysics
Identifiers
urn:nbn:se:liu:diva-127801 (URN)10.1098/rsfs.2015.0075 (DOI)000375410900001 ()27051506 (PubMedID)
Note

Funding agencies: Swedish Research Council; Swedish Diabetes Foundation; Linkoping Initiative within Life Science Technologies; CENIIT; Ostergotland County Council; EU [FP7-HEALTH-305707]; AstraZeneca

Available from: 2016-05-13 Created: 2016-05-13 Last updated: 2017-04-24Bibliographically approved
Rajan, M. R., Nyman, E., Kjölhede, P., Cedersund, G. & Strålfors, P. (2016). Systems-wide Experimental and Modeling Analysis of Insulin Signaling through Forkhead Box Protein O1 (FOXO1) in Human Adipocytes, Normally and in Type 2 Diabetes. Journal of Biological Chemistry, 291(30), 15806-15819
Open this publication in new window or tab >>Systems-wide Experimental and Modeling Analysis of Insulin Signaling through Forkhead Box Protein O1 (FOXO1) in Human Adipocytes, Normally and in Type 2 Diabetes
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2016 (English)In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 291, no 30, p. 15806-15819Article in journal (Refereed) Published
Abstract [en]

Insulin resistance is a major aspect of type 2 diabetes (T2D), which results from impaired insulin signaling in target cells. Signaling to regulate forkhead box protein O1 (FOXO1) may be the most important mechanism for insulin to control transcription. Despite this, little is known about how insulin regulates FOXO1 and how FOXO1 may contribute to insulin resistance in adipocytes, which are the most critical cell type in the development of insulin resistance. We report a detailed mechanistic analysis of insulin control of FOXO1 in human adipocytes obtained from non-diabetic subjects and from patients with T2D. We show that FOXO1 is mainly phosphorylated through mTORC2-mediated phosphorylation of protein kinase B at Ser(473) and that this mechanism is unperturbed in T2D. We also demonstrate a cross-talk from the MAPK branch of insulin signaling to stimulate phosphorylation of FOXO1. The cellular abundance and consequently activity of FOXO1 are halved in T2D. Interestingly, inhibition of mTORC1 with rapamycin reduces the abundance of FOXO1 to the levels in T2D. This suggests that the reduction of the concentration of FOXO1 is a consequence of attenuation of mTORC1, which defines much of the diabetic state in human adipocytes. We integrate insulin control of FOXO1 in a network-wide mathematical model of insulin signaling dynamics based on compatible data from human adipocytes. The diabetic state is network-wide explained by attenuation of an mTORC1-to-insulin receptor substrate-1 (IRS1) feedback and reduced abundances of insulin receptor, GLUT4, AS160, ribosomal protein S6, and FOXO1. The model demonstrates that attenuation of the mTORC1-to-IRS1 feedback is a major mechanism of insulin resistance in the diabetic state.

Place, publisher, year, edition, pages
Rockville, Maryland: American Society for Biochemistry and Molecular Biology, 2016
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:liu:diva-130998 (URN)10.1074/jbc.M116.715763 (DOI)000380584200033 ()27226562 (PubMedID)
Note

Funding agencies|Swedish Diabetes Fund, University of Linköping; Swedish Research Council; AstraZeneca

Available from: 2016-09-02 Created: 2016-09-02 Last updated: 2019-06-28Bibliographically approved
ul Hasan, K., Asif, M., Umair Hassan, M., Sandberg, M. O., Nour, O., Willander, M., . . . Strålfors, P. (2015). A Miniature Graphene-based Biosensor for Intracellular Glucose Measurements. Electrochimica Acta, 174, 574-580
Open this publication in new window or tab >>A Miniature Graphene-based Biosensor for Intracellular Glucose Measurements
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2015 (English)In: Electrochimica Acta, ISSN 0013-4686, E-ISSN 1873-3859, Vol. 174, p. 574-580Article in journal (Refereed) Published
Abstract [en]

We report on a small and simple graphene-based potentiometric sensor for the measurement of intracellular glucose concentration. A fine borosilicate glass capillary coated with graphene and subsequently immobilized with glucose oxidase (GOD) enzyme is inserted into the intracellular environment of a single human cell. The functional groups on the edge plane of graphene assist the attachment with the free amine terminals of GOD enzyme, resulting in a better immobilization. The sensor exhibits a glucose-dependent electrochemical potential against an Ag/AgCl reference microelectrode which is linear across the whole concentration range of interest (10 - 1000 mu M). Glucose concentration in human fat cell measured by our graphene-based sensor is in good agreement with nuclear magnetic resonance (NMR) spectroscopy.

Place, publisher, year, edition, pages
Elsevier, 2015
Keywords
Graphene; Bio-sensors; Glucose-oxidase; Intracellular sensors; Graphene oxide; Glucose Sensor
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Clinical Medicine
Identifiers
urn:nbn:se:liu:diva-121309 (URN)10.1016/j.electacta.2015.06.035 (DOI)000359873400073 ()
Available from: 2015-09-16 Created: 2015-09-14 Last updated: 2018-02-13Bibliographically approved
Jullesson, D., Johansson, R., Rohini Rajan, M., Strålfors, P. & Cedersund, G. (2015). Dominant negative inhibition data should be analyzed using mathematical modeling - re-interpreting data from insulin signaling.. The FEBS Journal, 282(4), 788-802
Open this publication in new window or tab >>Dominant negative inhibition data should be analyzed using mathematical modeling - re-interpreting data from insulin signaling.
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2015 (English)In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 282, no 4, p. 788-802Article in journal (Refereed) Published
Abstract [en]

As our ability to measure the complexity of intracellular networks has evolved, it has become increasingly clear that we need new methods for data analysis: methods involving mathematical modeling. Nevertheless, it is still uncontroversial to publish and interpret experimental results without a model-based proof that the reasoning is correct. In the present study, we argue that this attitude probably needs to change in the future. We illustrate this need for modeling by considering the common experimental technique of using dominant-negative constructs. More specifically, we consider published time-series and dose-response data which previously have been used to argue that the protein S6 kinase does not phosphorylate insulin receptor substrate-1 at a specific serine residue. Using a presented general approach to interpret such data, we now demonstrate that the given dominant-negative data are not conclusive (i.e. that in the absence of other proofs, S6 kinase still may be the kinase). Using simulations with uncertainty analysis and analytical solutions, we show that an alternative explanation is centered around depletion of substrate, which can be tested experimentally. This analysis thus illustrates both the necessity and the benefits of using mathematical modeling to fully understand the implications of biological data, even for a small system and relatively simple data.

Keywords
insulin signalling, dominant negative data, mathematical modelling
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:liu:diva-115805 (URN)10.1111/febs.13182 (DOI)000350288300011 ()25546185 (PubMedID)
Funder
Swedish Research Council
Available from: 2015-03-20 Created: 2015-03-20 Last updated: 2017-12-04
Sips, F. L. P., Nyman, E., Adiels, M., Hilbers, P. A. J., Strålfors, P., van Riel, N. A. W. & Cedersund, G. (2015). Model-Based Quantification of the Systemic Interplay between Glucose and Fatty Acids in the Postprandial State. PLoS ONE, 10(9), e0135665
Open this publication in new window or tab >>Model-Based Quantification of the Systemic Interplay between Glucose and Fatty Acids in the Postprandial State
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2015 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 9, p. e0135665-Article in journal (Refereed) Published
Abstract [en]

In metabolic diseases such as Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease, the systemic regulation of postprandial metabolite concentrations is disturbed. To understand this dysregulation, a quantitative and temporal understanding of systemic postprandial metabolite handling is needed. Of particular interest is the intertwined regulation of glucose and non-esterified fatty acids (NEFA), due to the association between disturbed NEFA metabolism and insulin resistance. However, postprandial glucose metabolism is characterized by a dynamic interplay of simultaneously responding regulatory mechanisms, which have proven difficult to measure directly. Therefore, we propose a mathematical modelling approach to untangle the systemic interplay between glucose and NEFA in the postprandial period. The developed model integrates data of both the perturbation of glucose metabolism by NEFA as measured under clamp conditions, and postprandial time-series of glucose, insulin, and NEFA. The model can describe independent data not used for fitting, and perturbations of NEFA metabolism result in an increased insulin, but not glucose, response, demonstrating that glucose homeostasis is maintained. Finally, the model is used to show that NEFA may mediate up to 30-45% of the postprandial increase in insulin-dependent glucose uptake at two hours after a glucose meal. In conclusion, the presented model can quantify the systemic interactions of glucose and NEFA in the postprandial state, and may therefore provide a new method to evaluate the disturbance of this interplay in metabolic disease.

Place, publisher, year, edition, pages
PUBLIC LIBRARY SCIENCE, 2015
National Category
Clinical Medicine
Identifiers
urn:nbn:se:liu:diva-121744 (URN)10.1371/journal.pone.0135665 (DOI)000360965800006 ()26356502 (PubMedID)
Note

Funding Agencies|European Union [305707]; Linkoping Initiative within Life Science Technologies; Ostergotland County Council; Swedish Research Council; AstraZeneca

Available from: 2015-10-06 Created: 2015-10-05 Last updated: 2017-12-01
Nyman, E., Rohini Rajan, M., Fagerholm, S., Brännmark, C., Cedersund, G. & Strålfors, P. (2014). A Single Mechanism Can Explain Network-wide Insulin Resistance in Adipocytes from Obese Patients with Type 2 Diabetes. Journal of Biological Chemistry, 289(48), 33215-33230
Open this publication in new window or tab >>A Single Mechanism Can Explain Network-wide Insulin Resistance in Adipocytes from Obese Patients with Type 2 Diabetes
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2014 (English)In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 289, no 48, p. 33215-33230Article in journal (Refereed) Published
Abstract [en]

The response to insulin is impaired in type 2 diabetes. Much information is available about insulin signaling, but understanding of the cellular mechanisms causing impaired signaling and insulin resistance is hampered by fragmented data, mainly obtained from different cell lines and animals. We have collected quantitative and systems-wide dynamic data on insulin signaling in primary adipocytes and compared cells isolated from healthy and diabetic individuals. Mathematical modeling and experimental verification identified mechanisms of insulin control of the MAPKs ERK1/2. We found that in human adipocytes, insulin stimulates phosphorylation of the ribosomal protein S6 and hence protein synthesis about equally via ERK1/2 and mTORC1. Using mathematical modeling, we examined the signaling network as a whole and show that a single mechanism can explain the insulin resistance of type 2 diabetes throughout the network, involving signaling both through IRS1, PKB, and mTOR and via ERK1/2 to the nuclear transcription factor Elk1. The most important part of the insulin resistance mechanism is an attenuated feedback from the protein kinase mTORC1 to IRS1, which spreads signal attenuation to all parts of the insulin signaling network. Experimental inhibition of mTORC1 using rapamycin in adipocytes from non-diabetic individuals induced and thus confirmed the predicted network-wide insulin resistance.

Place, publisher, year, edition, pages
American Society for Biochemistry and Molecular Biology, 2014
National Category
Clinical Medicine
Identifiers
urn:nbn:se:liu:diva-113198 (URN)10.1074/jbc.M114.608927 (DOI)000345636600015 ()25320095 (PubMedID)
Note

Funding Agencies|Swedish Diabetes Fund; University of Linkoping; Swedish Research Council

Available from: 2015-01-13 Created: 2015-01-12 Last updated: 2017-12-05
Johansson, R., Strålfors, P. & Cedersund, G. (2014). Combining test statistics and models in bootstrapped model rejection: it is a balancing act. BMC Systems Biology, 8(46)
Open this publication in new window or tab >>Combining test statistics and models in bootstrapped model rejection: it is a balancing act
2014 (English)In: BMC Systems Biology, ISSN 1752-0509, E-ISSN 1752-0509, Vol. 8, no 46Article in journal (Refereed) Published
Abstract [en]

Background: Model rejections lie at the heart of systems biology, since they provide conclusive statements: that the corresponding mechanistic assumptions do not serve as valid explanations for the experimental data. Rejections are usually done usinge.g. the chi-square test (χ2) or the Durbin-Watson test (DW). Analytical formulas for the corresponding distributions rely on assumptions that typically are not fulfilled. This problem is partly alleviated by the usage of bootstrapping, a computationally heavy approach to calculate an empirical distribution. Bootstrapping also allows for a natural extension to estimation of joint distributions, but this feature has so far been little exploited.

Results: We herein show that simplistic combinations of bootstrapped tests, like the max or min of the individual p-values, give inconsistent, i.e. overly conservative or liberal, results. A new two-dimensional (2D) approach based on parametric bootstrapping, on the other hand, is found both consistent and with a higher power than the individual tests, when tested on static and dynamic examples where the truth is known. In the same examples, the most superior test is a 2D χ2 vs χ2, where the second χ2-value comes from an additional help model, and its ability to describe bootstraps from the tested model. This superiority is lost if the help model is too simple, or too flexible. If a useful help model is found, the most powerful approach is the bootstrapped log-likelihood ratio (LHR). We show that this is because the LHR is one-dimensional, because the second dimension comes at a cost, and because LHR has retained most of the crucial information in the 2D distribution. These approaches statistically resolve a previously published rejection example for the first time.

Conclusions: We have shown how to, and how not to, combine tests in a bootstrap setting, when the combinatio is advantageous, and when it is advantageous to include a second model. These results also provide a deeper insight into the original motivation for formulating the LHR, for the more general setting of nonlinear and non-nested models. These insights are valuable in cases when accuracy and power, rather than computational speed, are prioritized.

Place, publisher, year, edition, pages
BioMed Central, 2014
Keywords
Model rejection, Bootstrapping, Combining information, 2D, Insulin signaling, Model Mimicry, Likelihood ratio
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
urn:nbn:se:liu:diva-106427 (URN)10.1186/1752-0509-8-46 (DOI)000335472800001 ()24742065 (PubMedID)
Available from: 2014-05-07 Created: 2014-05-07 Last updated: 2017-12-05Bibliographically approved
Östh, M., Öst, A., Kjölhede, P. & Strålfors, P. (2014). The Concentration of beta-Carotene in Human Adipocytes, but Not the Whole-Body Adipocyte Stores, Is Reduced in Obesity. PLoS ONE, 9(1), 85610
Open this publication in new window or tab >>The Concentration of beta-Carotene in Human Adipocytes, but Not the Whole-Body Adipocyte Stores, Is Reduced in Obesity
2014 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 1, p. 85610-Article in journal (Refereed) Published
Abstract [en]

We have examined the concentration of beta-carotene in the fat of isolated abdominal subcutaneous adipocytes obtained from lean (BMIless than23 kg/m(2)), non-obese with higher BMI (23 less than= BMIless than28 kg/m(2)), obese (BMI greater than= 28 kg/m(2)), and from a group of obese subjects with type 2 diabetes. The concentration of b-carotene was 50% lower in the adipocytes from the obese and obese/diabetic groups compared with the lean and non-obese groups. Interestingly, the total amount of beta-carotene in the adipocyte stores of each subject was constant among all groups. Triacylglycerol constituted 92 +/- 1% (by weight) of the adipocyte lipids in the lean group and this was increased to 99 +/- 2% in the obese group with diabetes (pless than0.05). The concentration of cholesteryl esters was in all cases less than0.1 g per 100 g of total lipids, demonstrating that mature human adipocytes have negligible stores of cholesteryl ester. Our findings demonstrate that adipocyte concentrations of beta-carotene are reduced in obese subjects. The lower concentrations in adipocytes from subjects with type 2 diabetes apparently reflect subjects obesity. Our finding that whole-body stores of beta-carotene in adipocytes are constant raises new questions regarding what function it serves, as well as the mechanisms for maintaining constant levels in the face of varied adipose tissue mass among individuals over a period of time.

Place, publisher, year, edition, pages
Public Library of Science, 2014
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-104290 (URN)10.1371/journal.pone.0085610 (DOI)000329862500259 ()
Available from: 2014-02-17 Created: 2014-02-14 Last updated: 2019-06-28
Nyman, E., Rohini Rajan, M., Fagerholm, S., Brännmark, C., Cedersund, G. & Strålfors, P. (2014). The insulin-signaling network in human adipocytes, normally and in diabetes: role of signaling through ERK1/2.
Open this publication in new window or tab >>The insulin-signaling network in human adipocytes, normally and in diabetes: role of signaling through ERK1/2
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2014 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Insulin acutely controls metabolism in adipocytes, but also nuclear transcription through the “mitogenic” signaling pathway mediated by Map-kinases ERK1/2 (ERK). The cellular metabolic response to insulin is attenuated in insulin resistance and type 2 diabetes, but whether this involves also signaling through ERK is unclear. Based on experimental data from primary mature human adipocytes from diabetic and nondiabetic individuals, we demonstrate a network-wide, model-based analysis of insulin signaling through ERK to phosphorylation of transcription factor Elk1 integrated with signaling for “metabolic” control. We use minimal modeling to analyze the idiosyncratic phosphorylation dynamics of ERK, i.e. a slow phosphorylation response that returns to basal in response to insulin, and conclude that sequestration of ERK is the simplest explanation to data. We also demonstrate a significant cross-talk between ERK and mTORC1 signaling to ribosomal protein S6 for control of protein synthesis. A reduced sensitivity and reduced maximal phosphorylation of ERK in response to insulin in the diabetic state can be explained by the same mechanisms that generate insulin resistance in the control of metabolism.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-104724 (URN)
Available from: 2014-02-24 Created: 2014-02-24 Last updated: 2014-02-24Bibliographically approved
Jufvas, Å., Sjödin, S., Lundqvist, K., Amin, R., Vener, A. V. & Strålfors, P. (2013). Global differences in specific histone H3 methylation are associated with overweight and type 2 diabetes.. Clinical Epigenetics, 5(1), Article ID 15.
Open this publication in new window or tab >>Global differences in specific histone H3 methylation are associated with overweight and type 2 diabetes.
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2013 (English)In: Clinical Epigenetics, E-ISSN 1868-7083, Vol. 5, no 1, article id 15Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Epidemiological evidence indicates yet unknown epigenetic mechanisms underlying a propensity for overweight and type 2 diabetes. We analyzed the extent of methylation at lysine 4 and lysine 9 of histone H3 in primary human adipocytes from 43 subjects using modification-specific antibodies.

RESULTS: The level of lysine 9 dimethylation was stable, while adipocytes from type 2 diabetic and non-diabetic overweight subjects exhibited about 40% lower levels of lysine 4 dimethylation compared with cells from normal-weight subjects. In contrast, trimethylation at lysine 4 was 40% higher in adipocytes from overweight diabetic subjects compared with normal-weight and overweight non-diabetic subjects. There was no association between level of modification and age of subjects.

CONCLUSIONS: The findings define genome-wide molecular modifications of histones in adipocytes that are directly associated with overweight and diabetes, and thus suggest a molecular basis for existing epidemiological evidence of epigenetic inheritance.

Place, publisher, year, edition, pages
BioMed Central, 2013
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:liu:diva-99450 (URN)10.1186/1868-7083-5-15 (DOI)000329455000001 ()24004477 (PubMedID)
Available from: 2013-10-18 Created: 2013-10-18 Last updated: 2017-12-06Bibliographically approved
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